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Integrated miRNA and mRNA expression profiling of mouse mammary tumor models identifies miRNA signatures associated with mammary tumor lineage

Transgenic Oncogenesis and Genomics Section, Laboratory of Cell Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
Genome biology (Impact Factor: 10.47). 08/2011; 12(8):R77. DOI: 10.1186/gb-2011-12-8-r77
Source: PubMed

ABSTRACT MicroRNAs (miRNAs) are small, non-coding, endogenous RNAs involved in regulating gene expression and protein translation. miRNA expression profiling of human breast cancers has identified miRNAs related to the clinical diversity of the disease and potentially provides novel diagnostic and prognostic tools for breast cancer therapy. In order to further understand the associations between oncogenic drivers and miRNA expression in sub-types of breast cancer, we performed miRNA expression profiling on mammary tumors from eight well-characterized genetically engineered mouse (GEM) models of human breast cancer, including MMTV-H-Ras, -Her2/neu, -c-Myc, -PymT, -Wnt1 and C3(1)/SV40 T/t-antigen transgenic mice, BRCA1(fl/fl);p53(+/-);MMTV-cre knock-out mice and the p53(fl/fl);MMTV-cre transplant model.
miRNA expression patterns classified mouse mammary tumors according to luminal or basal tumor subtypes. Many miRNAs found in luminal tumors are expressed during normal mammary development. miR-135b, miR-505 and miR-155 are expressed in both basal human and mouse mammary tumors and many basal-associated miRNAs have not been previously characterized. miRNAs associated with the initiating oncogenic event driving tumorigenesis were also identified. miR-10b, -148a, -150, -199a and -486 were only expressed in normal mammary epithelium and not tumors, suggesting that they may have tumor suppressor activities. Integrated miRNA and mRNA gene expression analyses greatly improved the identification of miRNA targets from potential targets identified in silico.
This is the first large-scale miRNA gene expression study across a variety of relevant GEM models of human breast cancer demonstrating that miRNA expression is highly associated with mammary tumor lineage, differentiation and oncogenic pathways.

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Available from: Ming Yi, Sep 03, 2014
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    • "The method called significance analysis of microarrays is used in several works10–15 to identify differentially expressed miRNAs. Different statistical tests are also employed to identify differentially expressed miRNAs.1,4–8,16–19 Xu et al20 used the particle swarm optimization technique for selecting important miRNAs that contribute to the discrimination of different cancer types. "
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    ABSTRACT: The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
    International Journal of Nanomedicine 09/2013; 8(Suppl 1):63-74. DOI:10.2147/IJN.S40739 · 4.38 Impact Factor
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    • "The method called significance analysis of microarrays is used in several works [11]‐[16] to identify differentially expressed miRNAs. Different statistical tests are also employed to identify differentially expressed miRNAs [1,4]‐[8,17]‐[20]. Xu et al. [21] used particle swarm optimization technique for selecting important miRNAs that contribute to the discrimination of different cancer types. "
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    ABSTRACT: The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer . Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. In this regard, a new miRNA selection algorithm, called µHEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The .632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. An important finding is that the µHEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the µHEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.
    BMC Bioinformatics 09/2013; 14(1):266. DOI:10.1186/1471-2105-14-266 · 2.67 Impact Factor
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    • "The MMTV-HER2/neu transgenic mouse tumor model has also been examined by global miRNA profiling [91]. By comparing to normal mouse mammary glands from pregnant mice and tumors driven by other oncogenes, miRNAs specifically expressed in MMTV-HER2/neu tumors are determined to be mmu-let-7d/7e/7f and mmu-miR-193, -185, -130b, -98, -691, -684, -469, and -539 (Table 1). "
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    ABSTRACT: The discovery of microRNAs (miRNAs) has opened up new avenues for studying cancer at the molecular level, featuring a post-genomic era of biomedical research. These non-coding regulatory RNA molecules of ~22 nucleotides have emerged as important cancer biomarkers, effectors, and targets. In this review, we focus on the dysregulated biogenesis and function of miRNAs in cancers with an overexpression of the proto-oncogene HER2. Many of the studies reviewed here were carried out in breast cancer, where HER2 overexpression has been extensively studied and HER2-targeted therapy practiced for more than a decade. MiRNA signatures that can be used to classify tumors with different HER2 status have been reported but little consensus can be established among various studies, emphasizing the needs for additional well-controlled profiling approaches and meta-analyses in large and well-balanced patient cohorts. We further discuss three aspects of microRNA dysregulation in or contribution to HER2-associated malignancies or therapies: (a) miRNAs that are up- or down-regulated by HER2 and mediate the downstream signaling of HER2; (b) miRNAs that suppress the expression of HER2 or a factor in HER2 receptor complexes, such as HER3; and (c) miRNAs that affect responses to anti-HER2 therapies. The regulatory mechanisms are elaborated using mainly examples of miR- 205, miR-125, and miR-21. Understanding the regulation and function of miRNAs in HER2-overexpressing tumors shall shed new light on the pathogenic mechanisms of microRNAs and the HER2 proto-oncogene in cancer, as well as on individualized or combinatorial anti-HER2 therapies.
    09/2013; 2(2):137-47. DOI:10.2174/22115366113029990011
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